An FPGA-implemented Associative-memory Based Online Learning Method

Fengwei An, Hans Jürgen Mattausch, Tetsushi Koide

Abstract—In this work, we propose an FPGA implemented associative-memory-based lazy learning method with a short/long-term memory concept which has an online learning capability. The conventional lazy or memory-based learning algorithm has low computational costs in the training stage since it simply stores the inputs and defers processing of training data until a query needs to be answered. However, it requires large storage capability, often pays high computational costs to answer a request, and is easily fooled by irrelevant attributes. To speed-up the query time, which is a deficiency of conventional lazy learning, we apply an associative memory which is implemented in an FPGA for searching the most similar data among the previously stored reference data in parallel. The proposed learning method can eliminate irrelevant attributes and reduce the storage requirements through a forgetting process in the short-term memory. When the online lazy learning model is applied to handwritten character recognition, the mismatch rate is reduced to 0.6% after a learning process with 450 alphabet letters learning and the number of reference is reduced by about 40% in comparison to the conventional method.